Pedestrian Detection in Driver Assistance Using SSD and PS-GAN

Abstract

Pedestrian detection is a critical challenge in the field of general object detection, the performance of object detection has advanced with the development of deep learning. However, considerable improvement is still required for pedestrian detection, considering the differences in pedestrian wears, action, and posture. In the driver assistance system, it is necessary to further improve the intelligent pedestrian detection ability. We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection. Firstly, we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics. Secondly, we propose a novel network architecture, namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector. Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent. At last, we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle. The results establish the validity of the approach.